REVIEW 2 cited by
Users Favor LLM-Generated Content -- Until They Know It's AI
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we investigate how individuals evaluate human and large langue models generated responses to popular questions when the source of the content is either concealed or disclosed. Through a controlled field experiment, participants were presented with a set of questions, each accompanied by a response generated by either a human or an AI. In a randomized design, half of the participants were informed of the response's origin while the other half remained unaware. Our findings indicate that, overall, participants tend to prefer AI-generated responses. However, when the AI origin is revealed, this preference diminishes significantly, suggesting that evaluative judgments are influenced by the disclosure of the response's provenance rather than solely by its quality. These results underscore a bias against AI-generated content, highlighting the societal challenge of improving the perception of AI work in contexts where quality assessments should be paramount.
Forward citations
Cited by 2 Pith papers
-
From Forensics to Ecosystems: Rethinking Watermarks for Generative AI Oversight
Watermarks should be repurposed from forensic identification of individual AI outputs to ecosystem-level measurement of aggregate synthetic content saturation.
-
Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
An argument, grounded in social-science measurement theory, that LLM-as-judge adoption has outpaced validity and reliability testing, with an analysis of four underlying assumptions.
Discussion (0). Continue with ORCID to comment.